A Survey on Mathematical Aspects of Machine Learning in GeoPhysics: The Cases of Weather Forecast, Wind Energy, Wave Energy, Oil and Gas Exploration

TL;DR

This survey reviews the application of machine learning techniques in geophysics, highlighting progress, future directions, and potential improvements like DataFlow-based implementations to accelerate performance.

Contribution

It provides a comprehensive overview of ML applications in geophysics, emphasizing recent successes and proposing future research directions and performance enhancement strategies.

Findings

ML has significantly advanced weather forecasting and resource exploration.

DataFlow paradigms can potentially accelerate ML implementations in geophysics.

Future research should focus on integrating ML with geophysical data for improved accuracy.

Abstract

This paper reviews the most notable works applying machine learning techniques (ML) in the context of geophysics and corresponding subbranches. We showcase both the progress achieved to date as well as the important future directions for further research while providing an adequate background in the fields of weather forecast, wind energy, wave energy, oil and gas exploration. The objective is to reflect on the previous successes and provide a comprehensive review of the synergy between these two fields in order to speed up the novel approaches of machine learning techniques in geophysics. Last but not least, we would like to point out possible improvements, some of which are related to the implementation of ML algorithms using DataFlow paradigm as a means of performance acceleration.